Yolo V10 cpp
Resumen
The next repository aims to provide a basic c++ script using std 17 over, to do it and consider the speed The code use OpenCv 4.9.0_8 and Onnx 1.17.1 to manipulate the image and inference the model. Note that Opncv don’t support a native integration because yolov10 integra A top K layer in their architecture.
Prepare the code
- Download de model you want
- yolov10n
- yolov10s
- yolov10m
- yolov10b
- yolov10l
- yolov10x
python download_model.py --model {MODEL_SELECTED}
Install packages
conda create -n yolov10 python=3.9
conda activate yolov10
git clone https://github.com/THU-MIG/yolov10
cd yolov10
pip install -r requirements.txt
pip install -e .
cd ..
Convert model
yolo export model=yolov10n.pt format=onnx
Dependencies
- ffmpeg
- Opnecv
- onnxruntime
brew install ffmpeg
brew install opencv
brew install onnxruntime
- Ubuntu: Unfortunately, onnx runtime is no available using native apt-get
You can use python
sudo apt-get update
sudo apt-get install python3-pip
pip3 install onnxruntime
dotnet
dotnet add package Microsoft.ML.OnnxRuntime
How to run this code
- Using Cmake, Recommended
mkdir build
cd build
cmake ..
make
- Run the following command
static images
./yolov10_cpp [MODEL_PATH] [IMAGE_PATH]
realtime
./yolov10_cpp_video [MODEL_PATH] [SOURCE]
Results
our cpp binding | python binding
source = Apple M3 PRO
| Command Line Execution |
Resource Utilization |
./yolov10_cpp ../yolov10n.onnx ../bus.jpg |
0.46s user, 0.10s system, 94% CPU, 0.595s total |
yolo detect predict model=yolov10n.onnx source=bus.jpg |
1.69s user, 2.44s system, 291% CPU, 1.413s total |
Future plans
- Modularize the components. ✅
- Make a example to video real time. ✅
- Support Cuda. ?
Inspiration
Ultraopxt
Reference
[1] Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/2405.14458
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